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F-Coref: Fast, Accurate and Easy to Use Coreference Resolution

F-Coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover

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Experiments

Model Runtime Memory
Joshi et al. (2020) 12:06 27.4
Otmazgin et al. (2022) 06:43 4.6
+ Batching 06:00 6.6
Kirstain et al. (2021) 04:37 4.4
Dobrovolskii (2021) 03:49 3.5
F-Coref 00:45 3.3
+ Batching 00:35 4.5
+ Leftovers batching 00:25 4.0
The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2.

Citation

@inproceedings{Otmazgin2022FcorefFA,
  title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution},
  author={Shon Otmazgin and Arie Cattan and Yoav Goldberg},
  booktitle={AACL},
  year={2022}
}

F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)

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Dataset used to train biu-nlp/f-coref

Evaluation results